The first condition represents our baseline, in which we do not use category-based alignment in the word learning model by setting λ(w) = 1 in Eqn.

here we adopt an online version of the model proposed in Chrupała 2011, a method of soft word class learning using Latent Dirichlet Allocation.


this advantage can result from the fact that our categories are more fine-grained (if also more noisy) than POS categories, which sometimes yields more accurate predictions.


OPTIM and RANK require the setting of several parameters: (k, k , α, β) for OPTIM and (k, k , γ) for RANK.

taking into consideration the conditions mentioned the sentiment-inference problem can be formulated as an optimisation problem: After the substitutions α = ak + bk and β = b a the final optimisation problem can be written as: where β defines the relative weight between labeled and unlabeled neighbours, while α controls the weight of the graph-based solution with respect to the primarily obtained supervised sentiment scores.


Second, unlike other suggested methods, this approach can be easily adapted to multiple classes, which makes it possible to classify documents using finer-grained sentiment scales.

taking into account only the quantitative threshold we ignore discrepancies Figure 4: Pairwise document similarity distributions inside domains (A) and across domains (B) in graph connectivities inside and across domains and may bring "bad" neighbours to participate in decision-making.


We describe the base features below and use † and ‡ to denote the features used for C E−T and C E−E , respectively.

we noticed that the ACE 2005 corpus contains the annotation that we are interested in.


It is important to note that our interval-based formulation is more concise in terms of the number of variables and constraints needed in the ILP relative to time expression-based (or timepoint-based) formulations used in previous work (Chambers and Jurafsky, 2008).

we also measured the overall performance of the systems by computing the average of the performance of the classifiers.


To initialize this model, we combined the final instance of DBM-2 with uniform multinomials for punctuation-crossing attachment probabilities (see §2.3).

further boosts to accuracy could come from employing more sophisticated optimization algorithms, e.g., better EM (Samdani et al., 2012), constrained Gibbs sampling (Mareček and Zabokrtský, 2011) or locally-normalized features .


include parenthesized expressions that are marked as noun-phrases, such as (See related story: "Fed Ready to Inject Big Funds": WSJ Oct. 16, 1989); false negatives can be headlines having a main verb, e.g., Population Drain Ends For Midwestern States.

nevertheless, many popular grammars for unsupervised parsing behave as if a word had to generate all of its children (to one side) -or at least their count -before allowing any of these children themselves to recurse.


Parser projection is by far the most successful approach to date and we hope that it too may stand to gain from our modeling improvements.

we presuppose that all sentences are assigned to one of two classes: complete and incomplete (comp ∈ {T, F}, for now taken as exogenous).


The grammar learned can then be used to infer the most probable native language that a given text written in a second language is associated with.

we derive two adaptor grammar-based language models.


Shahaf and Guestrin (2010), for example, assume the thread endpoints are specified, and Chieu and Lee (2004) require a set of query words.

as in clustering and topic modeling, our goal is to provide an organizing perspective into otherwise overwhelming amounts of information.


Many works have accordingly attempted to identify text units that are as parallel as possible from large corpora, and the task of measuring semantic textual similarity, which can find many uses, has received some attention lately (Agirre et al., 2012).

the main guidelines that they had to follow were that sure and possible paraphrases must be distinguished, smaller alignments were to be prefered but any-to-any alignments may be used, and sentences should be aligned as much as possible.


Given such a scoring function, parsing amounts to solving: subject to y ∈ Y.

for the purposes of our work this mapping is not important, and hence we omit details.


These systems scale up to hundreds of predicates, but have much shallower semantic representations than semantic parsers.

it is solved by finding the maximum probability assignment , then choosing values for y and z that satisfy the weak supervision constraints.


Cantonese) data, as well as some parallel data of resource-poor and resource-rich languages.

we assume that a phrasẽ v k is generated independently by each phrasew r k .


Specifically, if the rank value of the pronoun or the rank value of one or both of the candidate antecedents cannot be determined, the values of all three binary features will be set to zero for both i 1 and i 2 .

in our example, we would create two pairs, (punish, try-s) and (punish, escapes).


Hence, if the Stanford resolver decides to resolve the target pronoun, it will resolve it to one of the two candidate antecedents.

it may not be good at handling pronouns whose resolution requires an understanding of the connection between the facts or events described in the two clauses of a sentence.


The limited range of the first order linear chain model could also have played a part in the poor performance of the CRF models.

the final decision for each quote is then just the speaker which is predicted by the sequence with the largest joint probability.


We can also obtain similar experimental results over Y Ans and O Hlth.

l; and for Simp-hlDA and SSHlDA, they need another parameter-the height of the hierarchical observed labels, i.e l. The h-clustering does not have any height parameters, thus its FScore will keep the same values at different height of the topical tree.


In a manner similar to RE, there is a strong correlation between the presence of an SRL relation and there existing an underlying syntactic dependency, though this is not always expressed as directly as a 1-to-1 correspondence.

alternatively we can describe the more structured constituency formalism by setting up a representation over span variables: boolean variables such that Span(i, j) = true iff there is a bracket spanning i to j 1 .


We evaluate these models using the 2005 Automatic Content Extraction (ACE) data set (Walker, 2006), using the English (dual-annotated) and Chinese (solely annotator #1 data set) sections.


LCLR aims to provide a flexible latent structure for increasing the representational power of the model in a useful way, and is demonstrated on tasks and domains where data availability is not a key concern.

• Let {Role(i, j, λ) : λ ∈ L, and 0 ≤ i < j ≤ n} be O(|L|n 2 ) boolean variables such that Role(i, j, λ) = true iff Arg(i, j) is true and takes the role label λ.


The algorithm greedily selects these edges in an attempt to quickly approximate the minimal set of tag bigrams needed to accomplish this goal.

this greatly simplifies the job of a typesupervised HMM: it no longer must entertain entries for uncommon word-tag pairs (or mistaken pairs due to annotation errors), which otherwise stand on equal footing with the common ones.


Our experiment training an HMM without minimization (scenario 3) resulted in 1531 "a" tokens being tagged LS, 731 as SYM, and only 32 tagged as DT.

as is often noted (including by the authors themselves), many papers that work on learning taggers from tag dictionaries make unrealistic assumptions about the tag dictionaries they use as input (Toutanova and Johnson, 2008;Ravi and Knight, 2009;Hasan and Ng, 2009).


The first one is the edge pruning threshold during graph construction; the second one is the weight α in SCAN algorithm; the third one and the forth one are the combination parameters in the final similarity function.

the experimental results show that the corpus level topic information provides more stable evidences for discriminative features and our method outperforms the state-of-the-art systems on three WePS datasets.


These two incorrect links hinder the extraction of a good minimal rule "毡房 " and that of a good composed rule "牧民, 的 NP(DT(the), NN(herdsmen), POS('s)) ".

the syntactic parser we used in this paper is Berkley parser, with the grammar trained on WSJ corpus, and the training method follows Petrov and Klein (2007).


 step3: Perform forced alignment on training data with to get parse trees for target sentence of training data.

some SL words yielded by a SL parse tree node may not be traced to, via alignment links, some TL words with legitimate syntactic structure.


However, this would be quite slow: each iteration of the training procedure requires iterating through all n training sentences 6 once for each of the m candidate transformations, for a total cost of O(nm) where m grows (albeit sublinearly) with n.  Table 2. most useful transformations almost by definition are ones that are triggered the most frequently, any reasonably sized training set is likely to contain them, and so it is not actually likely that dramatically increasing the size of the training set will yield particularly large gains.

for this dataset, the initial state annotations (parse trees) were manually created by trained human annotators, as were the word alignments used to compute the agreement   score.


To the extent that there are simply divergences in the syntactic structure of the two languages, it will often be impossible to construct syntax trees that are simultaneously in full agreement with monolingual linguistic theories and with the alignments between sentences in both languages.

we use this method to learn a transformation sequence for the English trees in a set of English to Chinese MT training data.


From experiments on the development set, we set the relative strength of the consistency component β = 10.

in this case, the JCC achieves a 36% error reduction over the basic aggregation model.


While those studies discussed the same problem as this paper, they did not point out the importance of the constancy in identifying unique relations (cf.

we could not confirm the effect of using multiple time windows of different sizes in the constancy classification task.


Our research here takes initial steps toward a future where the vast universe of entities that are not prominent enough to include in manually-authored knowledge bases is analyzed automatically instead of being left behind.

we found that while low Slope (especially with higher R 2 ) often indicated non-entity, there were numerous cases where higher Slope did not necessarily indicate entity.


Our experiments on the Instructional corpus consider the same 26 primary relations (e.g., GOAL:ACT, CAUSE:EFFECT, GENERAL-SPECIFIC) used in (Subba and Eugenio, 2009) and also treat the reversals of non-commutative relations as separate relations.

these results are also consistent with the mean results over 10-folds.


A dominance set D (shown at the bottom of Figure 5 for our example) contains these attachment points of the EDUs in a DS-LST.

it is interesting to observe how much our full system is affected by an automatic segmenter on both RST-DT and the Instructional corpus (see Table 2 and Table 5).


It is widely agreed that identifying paraphrases is a core task for natural language processing, including applications like document summarization (Barzilay et al., 1999), Recognizing Textual Entailment (Dagan et al., 2005), natural language generation (Zhao et al., 2010;Ganitkevitch et al., 2011), and machine translation (Marton et al., 2009).

for evaluation, we use all episodes except no.


We report the percentage of items where the judges selected both, the lifted, or non-lifted sentence, alongside with the average flu-ency score (0-100) and preference strength (0-50).

3 This means that we are able to predict the correct lifting for most (but not all) of the non-projective edges in our data sets (cf .


It clearly emerges from this figure that the range of improvements obtainable from lifting is closely tied to the general linearization quality, and also to word order properties of the languages.

czech, German, and Dutch show the highest percentage of nonprojective edges.


The suffered loss Creating the word order of a sentence.

contrary to our work, they use phrase-structure syntax and focus on a limited number of cases of crossing branches in German only.


There are several ways to formalize the relationship between the distances and co-occurrence statistics, in this paper we use the following: x,y where x,y is the squared distance between the embeddings of x and y,p(x) andp(y) are empirical probabilities, and Z = x,yp (x)p(y)e −d 2 x,y is a normalization term.

section 3 describes the dataset and the construction of the substitute vectors.


In this case, "John" does not have many translations, so the likelihood of eliminating the correct hypothesis.

minimizing such an objective function would be intractable due to reordering, since the probability assigned to a phrase pair in a sentence pair by each model would depend on the positioning of all other phrase pairs used in the sentence.


There is no difference between absolute and relative pruning methods, except that the two relative methods (Thres and Hist) are limited by the number of source phrases.

a phrase-based system selects among different segmentations of the source language sentence into phrases.


In preliminary experiments, we found that most of the gain came from unigrams and bigrams, with little to no additional gains from trigrams.

we gratefully acknowledge the support of Defense Advanced Research Projects Agency (DARPA) Machine Reading Program under Air Force Research Laboratory (AFRL) prime contract no.


It is built on the backbone of weighted edit distance models, but learns to weight edit operations in a probabilistic regression framework.

we limited our experiments to bigram pFSM and pPDA models, and pruned the paraphrase table adopted from TERplus 5 to unigrams and bigrams, resulting in 2.5 million paraphrase pairs.


Different group comparisons, same group comparisons, and system combination comparisons form distinct curves.

comparisons between systems generated using the same base model type and comparisons between systems generated using different base model types are shown separately.


Public competitions, such as the well-known CoNLL shared tasks, provide one natural way to obtain a variety of system outputs on the same test set.

it appears that they do achieve higher confidences at lower metric gains.


for example, doubling the test size will not obviate the need for significance testing.


We use the paired bootstrap 1 (Efron and Tibshirani, 1993) because it is one 1 Riezler and Maxwell (2005) argue the benefits of approximate randomization testing, introduced by Noreen (1989).

comparisons between systems entered by the same research group and comparisons between systems entered by different research groups are shown separately.


Generating all the paths for millions of queries over a graph with millions of concepts and edges further complicates the computational issues.

the model parameters to be learned are the vector θ = θ π π∈B .


To circumvent this problem, we first index all parsed sentences by the concepts that they mention.

for example, for profession, = 5 gives a MRR of 0.589.


Hearst (1992) first noticed that patterns such as "NP and other NP" and "NP such as NP" often imply hyponym relations (NP here refers to a noun phrase).

we then use a within-document coreference resolver comparable to that of Haghighi and Klein (2009) to group referring NPs into co-referring clusters.


In those approaches, KB tuples of the relation to be extracted serve as positive training examples to the extraction rule induction algorithm.

the proposed random walk model can be an effective way to automate knowledge acquisition from the web.


For example, we observed that a small city in UK and another small city in Germany share common hypernyms such as city, location, and place.

the PS graph is generated by adopting both sentence lexical patterns and HtML tag patterns (Hearst, 1992;Kozareva et al., 2008;Zhang et al., 2009;Shi et al., 2010).


Recently, Kok and Domingos (2008) proposed Semantic Network Extractor (SNE), which generates argument semantic classes and sets of synonymous relation phrases at the same time, thus avoiding the requirement of tagging relation arguments of predefined types.

the web has become a massive potential source of such relations.


In practice, the sizes of partitions are not equal.

the similarity between the two cities is large according to the hypernymy graph, while their similarity in the DS graph and the PS graph may be very small.


Determining that these correspond to a PP attachment error from just the labels of the missing and extra nodes is difficult.

using this approach, we presented the first detailed examination of the errors produced by a wide range of constituency parsers for English.


By automatically classifying parse errors we are able to consider the output of multiple parsers on thousands of sentences.

other significant types have not received as much attention, such as clause attachment and modifier attachment.


There is no guarantee for the coherence of a text if it is produced by simply putting together stand-alone sentences, no matter how well-translated, without adequate intersentential connection.

(1995), and Nakaiwa and Shirai (1996), present approaches to resolving Japanese zero pronouns and to integrating them into a Japanese-English transferred-based MT system.


By focusing on a typical type of cohesion, i.e., lexical cohesion, we have shown that its use frequency is a significant factor to differentiate HT from MT and MT outputs of different quality from each other.

mETEOR is strong in unigram matching, with multiple strategies to maximize the matching rate be-tween mT output and reference translation.


Therefore, to answer the point query (QUERY (x)), CM returns the minimum over all the d positions x is stored.

finally, we demonstrated CM-CU has better results on all three tasks: pseudo-words evaluation for selectional preferences, finding semantic orientation task, and distributional similarity.


Often, computing marginal estimates of a model is computationally intractable due to the normalization constant Z, while maximum a posteriori (MAP) is prohibitive due to the search space of possible configurations.

instead of evaluating the log-score ψ of the model exactly, this paper proposes a Monte-Carlo estimation of the log-score.


Factors  As the body of published scientific work continues to grow, author coreference, the problem of clustering mentions of research paper authors into the real-world authors to which they refer, is becoming an increasingly important step for performing meaningful bibliometric analysis.

we observe consistent speed improvements as stochasticity is improved, with uniform sampling and confidence-based sampling performing competitively.


Allowing small violations to the conditions of the theorems provide an even higher improvement in speedup (over 3), without loss of performance.

note that 45 n quickly outgrows the number of sentences as n increases.


each sentence separately and runs the inference algorithm to predict the parse tree.

the conditions of theorems 2 and 3 check whether a collection of linear (in)equalities has a feasible solution using a linear program solver.


By proposing a common framework for sampling and optimization our approach has the advantage that we do not need separate skills or expertise to solve the two problems.

considering sampling, we suppose that the first sample from q (0) produces x 1 = (the, two, dog, barked), marked with bold edges in the drawing.


Section 3 outlines the basic machinery for pattern extraction.

a SOL pattern contains only the n-grams that appear frequently in the corpus and the remaining word sequences are replaced by wildcards.


Also, the technique is not suitable for inferring semantic subsumptions.

in this paper, we present PATTY, a large resource of relational patterns that are arranged in a semantically meaningful taxonomy, along with entity-pair instances.


Each testing method performs at its best when using models trained with the same method.

in our parser, EP is implemented as follows.


In addition, modulo backoff or smoothing, that unstructured symbol would often have rewrite parameters entirely distinct from, say, the indefinite but otherwise similar variant of the symbol (Klein and Manning, 2003).

we will approximate each model f m by its correspondingf m .


If we compile all annotation components into unstructured annotations, we can end up with a total grammar size of O(A M G 0 ), and so in general parsing time scales exponentially with the number of annotation components.

pCFGs can grow exponentially as additional annotations are added to an initially simple base grammar.


Intuitively, the 5-gram model is surprised, by more than an order of magnitude, to see "the" without matching words that precede it.

kneser-Ney smoothing (kneser and Ney, 1995) conditions lower-order probabilities on backing off.


The closer the initial state is to an optimum, the less work remains to be done for the algorithm.

the framework makes it possible to exploit decomposability for efficient scoring without imposing any particular decomposition on the features as beam search does.


Our bottom-up left-to-right decoder employs the same features as the traditional decoder: rule probability, lexical probability, language model probability, rule count and word count.

3 Bottom-Up Left-to-Right Decoding We propose a novel method of left-to-right decoding for tree-to-string translation using a bottom-up parsing strategy.


If the vectors have dimensionality n, then each word's matrix has dimensionality X ∈ R n×n .

assigning vector-matrix representations to all words instead of only to words of one part of speech category allows for greater flexibility which benefits performance.


We represent a word as both a continuous vector and a matrix of parameters.

only the MV-RNN has enough expressive power to allow negation to completely shift the sentiment with respect to an adjective.


This work has been extended in (Mohammed et al., 2011) to include a study of antonymy based on crowd-sourcing experiments.

to illustrate this, we have performed SVD with the aid of the Encarta thesaurus developed by Bloomsbury Publishing Plc.


In contrast, the WordNet-based methods (Lines 1-3) attempted 936 questions.

we derive this representation with the aid of a thesaurus and latent semantic analysis (LSA).


We first sample pairs of antonyms from the thesaurus to create the training data.

as noted by Landauer, 2002), there is no notion of antonymy; words with low or negative cosine scores are simply unrelated.


Specifically, given a row element r, a column element c and a counting function C(r, c), then where C(r, c) is how many times r cooccurs with c, C(r, * ) is the total count of r, C( * , c) is the total count of c, and C( * , * ) is the cumulative cooccurrence count of any r with any c. The dimensionality of the space was reduced using Singular Value Decomposition (SVD), as in Latent Semantic Analysis and related distributional semantic methods (Landauer and Dumais, 1997;Rapp, 2003;Schütze, 1997).

we predict that the nearest neighbors are more dispersed for adjectives that cannot be modeled as first-order properties (i.e., intensional adjectives), than for those that can (here, the color terms).


Though observed AN vectors should not be regarded as a gold standard in the sense of, for instance, Machine Learning approaches, because they are typically sparse 10 and thus the vectors of their component adjective and noun will be richer, they are still useful for exploration and as a comparison point for the composition operations (Baroni and Lenci, 2010;Guevara, 2010).

an examination of the nearest neighbors of the composed ANs suggest that alm captures the semantics of adjective composition in this case to a larger extent than mult.


First, the sheer ratio of positive to negative samples fluctuates drastically.

we thank the anonymous EMNLP reviewers for constructive comments.


We note that both IC R i and IC E i can have the values +1 and -1 active at the same time if intermediate sieve classifiers generated conflicting predictions.

we develop a novel approach for entity-based inference.


We are interested in a more challenging baseline, the co-referent pairs.

our goal is to apply the pairwise classifier on pairs in prescribed order and to propagate the knowledge across mentions.


A Markov logic network consists of a set of first-order clauses (which we will refer to as formulas in the rest of the paper) just like in first-order logic.

interested readers can refer to the literature review by Ng (2010).


Other issue-specific features are IVERB and IHEAD.

this is a high baseline because in our data 84.1% of the antecedents lie within the adjacent sentence.


The present work is a step towards resolving abstract anaphora in written text.

first, the anaphors refer to abstract concepts that can be expressed with different syntactic shapes which are usually not nominals.


These portals aim to provide highly focused access to this information by directly returning pertinent question and answer (Q&A) pairs to the users questions, instead of a long list of ranked URLs.

in ETLM qa , e had an option of mapping to ne in q.


Taxonomies are widely used for knowledge standardization, knowledge sharing, and inferencing in natural language processing (NLP) tasks (Harabagiu et al., 2003;Szpektor et al., 2004).

monothetic approaches organize the concepts into hierarchies and link documents to related concepts.


The first step is to extract the concepts and the second is to organize the concepts.

another common function of taxonomies, browsing, has received little attention in the NLP community.


if only α is nonzero, this is a linear transformation of buzzed  words' tf-idf weights.


Domain differences can introduce errors in a number of ways (Ben-David et al., 2007;Ben-David et al., 2009).

ignoring the significance test results for now, overall the results indicate that knowing the true domains is useful for MDL algorithms.


This is not surprising in light of the fact that the main purpose of natural language is communication and in the course of language evolution the selection pressure for more efficient communication would favor unambiguous languages.

the fact that Viterbi EM is a special case of our approach also gives an explanation of the advantage of Viterbi EM observed in previous work: it is because Viterbi EM implicitly utilizes unambiguity regularization.


However, the goal of DA is to improve the optimization of a non-concave objective function, which is achieved by setting the exponent in the E-step to a value close to 0, so that the distribution of the hidden variables becomes nearly uniform and the objective function becomes almost concave and therefore easy to optimize; this exponent is then gradually increased to 1 to optimize the original objective function.

here we incorporate unambiguity regularization into mean-field variational inference.


the goal of DA is to improve the optimization of a non-concave objective function, which is achieved by setting the exponent in the E-step to a value close to 0, so that the distribution of the hidden variables becomes nearly uniform and the objective function becomes almost concave and therefore easy to optimize; this exponent is then gradually increased to 1 to optimize the original objective function.


Another benefit of utilizing parsing is to speed up learning and inference.

by comparing the extraction results across different methods, we see that full parsing provides many benefits for modeling segment boundaries and improving the prediction precision for opinion expression extraction.


We implement efficient inference using an extension of Viterbi algorithm to segments.

(2007) presents the state-of-the-art sequence labeling approach on the tasks of DSE and ESE extraction.


(2) The Chief Minister [said] [DSE] that [the demon they have reared will eat up their own vitals] [ESE] .

we explore the use of semi-CRFs, which can assign labels to segments instead of tokens; hence, features can be defined at the segment level.


In particular, compared with previous syntax-based methods, our method can effectively avoid noises from parsing errors when dealing with informal texts in large Web corpora.

we also perform a significant test, i.e., a t-test with a default significant level of 0.05.


To extract opinion targets, many studies regarded opinion words as strong indicators Popescu et al., 2005;Liu et al., 2005;Qiu et al., 2011;Zhang et al., 2010), which is based on the observation that opinion words are usually located around opinion targets, and there are associations between them.

to prove the effectiveness of our method, we select the following state-of-art unsupervised methods as baselines for comparison.


These reviews not only provide customers with useful information for reference, but also are valuable for merchants to get the feedback from customers and enhance the qualities of their products or services.

the detailed statistical information of this dataset is also shown in table 2.


Given a review sentence, one option is to add the corresponding features to every item matching this sentence, with a value equal to the similarity coefficient.

we tune the regularization coefficient by choosing the value that minimizes development set loss (mean squared error and log loss, respectively).


They argued that, due to the ubiquity of food writing across cultures, ethnic groups, and social classes, studying the use of language for describing food can provide perspective on how different socioeconomic groups selfidentify using language and how they are linguistically targeted.

great place to get fast food that tastes good .


Our work capitalizes on manually designed conversion schemes to automatically create mappings for new languages.

in fact, the universal tagsets manually induced by  and by Naseem et al.


If we know that the target language has a particular typological feature, we expect its universal tags to obey the given relative ordering.

other properties concern PoS tag per sentence statisticse.g., every sentence has to have at least one verb.


This result is encouraging in other contexts as well.

we estimate these two statistics from the source language and denote them by E Sv , V Sv .


The reason is that words with tag "NN" or "NR/JJ" have the similar usage/contexts in Chinese.

the analysis of Chinese-English mixed texts is rarely involved in previous literature.


This language explosion clearly forces researchers to focus on the challenging problem of being able to analyze and understand text written in any language.

their main difference, instead, lies in the range of translations which are assumed to be valid: that is, while CL-LS assumes no predefined sense inventory (i.e., any translation can be potentially correct), CL-WSD makes use of a sense inventory built on the basis of the Europarl corpus (Koehn, 2005).


BabelNet  follows the structure of a traditional lexical knowledge base and, accordingly, consists of a labeled directed graph whose nodes represent concepts and named entities, and whose edges express semantic relations between them.

the sense that is consistently activated across all graphs is the correct one -i.e., bank EN n as financial institution -which is in fact the sense selected by our multilingual approach by means of combining the scoring distributions from all these graphs.


The best sense for a given term t is calculated as follows: that is, we select as the most suitable gloss for t the one which maximizes the product of its domain relevance score by its domain P P V d value.

many of them are domainspecific meanings for the general concept of "the act of restoring", with the BIOLOGY and GEOGRA-PHY senses being very similar.


The existing approach closest to ours is that of Huang and Riloff (2010), who devised a bootstrapping approach to induce semantic class taggers from domain text.

our approach tackles cross- domain ambiguity, by working with virtually any set of domains and minimizing the requirements by harvesting domain terms and definitions from the Web, bootstrapped using a small number of seeds.


CCA is concerned with describing linear dependencies between two sets of variables of relatively low dimensionality.

note that all models in Table 2 fall short of the human upper bound which we measured by calculating the reliability of Nelson et al.


Furthermore, we add nodes with a fixed label to incorporate contexts seen in the training data.

state-of-the-art statistical parsers and POs taggers perform very well when trained with large amounts of in-domain data.


We choose sets T c corresponding to the c'th unknown word type in the corpus.

the MRF we use in the experiments has a simple naive Bayes structure with the consensus node connected to all relevant word nodes.


From the perspective of the unified parser, linguistic units are given the same labels as long as they function similarly (e.g, they have the same parts-of-speech).

instead, a new unified parsing algorithm is given in this section.


the thesis of this paper, i.e, unified parsing of Chinese word and phrase structures, bears a deeper meaning.


For German, we obtain 89.05 LAS and 97.78 POS, which in both cases is substantially better than in the CoNLL shared task.

one final thing to note about the inference algorithm is that the notion of permissibility for a transition t out of a configuration c can be used to capture not only formal constraints on transitions -such as the fact that it is impossible to perform a SHIFT p transition with an empty buffer or illegal to perform a LEFT-ARC d transition with the special root node on top of the stack -but also to filter out unlike-ly dependency labels or tags.


Besides the best performance, we further compute the average of the top 10 results of each method by tuning parameters to check the average perfor- Table 6: Performance (average F) on multiple streams. "

users often embed a uRL link in the tweets to help others know more about the corresponding information.


Based on these definitions, our task is to try to identify event-related bursts via multiple social media activity streams.

wayne Xin Zhao, Rishan Chen, Kai Fan, Hongfei Yan, and Xiaoming Li.


To seek the optimal state sequence, we can minimize Equation 3.

this is why we adopt a different evaluation approach.


In Proceedings of the 20th ACM international conference on Information and knowledge management, CIKM.

to generate the golden standard, given a query, the judges first manually generate a candidate list of external events 7 ; then for each event, they look into the tweets within the corresponding period and check whether there is a surge on the frequency of tweets.


Table 7 summarizes the results and gives the accuracy of the all male baseline as well as of the propagation procedure (Prop-acc).

the exact procedure is as follows: given user u connected with videos V u = {v 1 , ..., v m }, there are m gender distributions sent to u: to address the skewness in the data, i.e., the fact that 70% of our users (62/(26 + 62)) with known gender are male, we select the female gender if (a) it got more than zero mass and at least as much mass as male:p(f ) > 0 ∧p(f ) ≥p(m), or (b) it got at least τ of the mass:p(f ) ≥ τ .


Note that datasets with different vocabulary size can lead to different entropy: the entropy of picking a word from the vocabulary uniformly at random would have been different.

consider a stimulus-response pair, where the stimulus is a sequence of tokens s = (s 1 , s 2 , ..., s m ), and the response is a sequence of tokens r = (r 1 , r 2 , ..., r n ).


recall from Section 4 that our comments dataset contains roughly 237K repliers and 357K original com- menters.


Nonetheless, using the centroid has the benefit of making a uniform grid less sensitive to cell size, such that larger cells can be used more reliably -especially important when there are few training documents.

the resulting leaves of the k-d tree form a patchwork of rectangles which cover the entire earth.


A uniform grid with large cells will mash Queens and Brooklyn together, while small cells will create unnecessarily sparse regions in Montana.

when choosing representative locations for the leaves of a k-d tree, it is quite important to use the centroid because the leaves necessarily span the entire earth and none are discarded (since all have a roughly similar number of documents in them).


Given query q, for any alignment A , where q At is the concatenation of q a 0 to q at and c At is the concatenation of c a 0 to c at .

we know from empirical experience that the source probability and the transformation probability are the two most important features in query spelling correction.


Without loss of generality, let us aim at learning a prediction function f : X → Y that maps input x ∈ X to an output y ∈ Y with latent structural information h ∈ H. The decision function is of the following form: where Ψ(x, y, h) is the set of feature functions defined jointly over the input x, the output y and the latent variable h. w is the parameter of the model.

the value of the feature (which is computed in an accumulative manner) cannot increase as the candidate is extended with a new term at any search step.


For a tree t rooted at N R with a height n, let T be the set of leaf nodes, and let F be the set of furcation nodes, and let ξ(N i , N j ) denote the length of the shortest path from N i to N j .

type-I Identification -Simple/Complex/ Compound/Complex-Compound: PCFG trees do not provide this information directly, hence we must construct an algorithm to derive it.


If the size of the training set is large enough, named entities and linguistic features as well as temporal constraints will improve the overall accuracy significantly.

case 1: The extraction is associated with an absolute temporal expressions with year mentions in the sentence.


When only 1,000 documents are initially labeled with timestamps, the confidence boosting model can propagate their timestamps to more than 400,000 documents with an accuracy of 0.494, approximately 12.8% relative improvement over the BFS counterpart, which proves effectiveness of the confidence boosting model.


This part of the model is parametric, operating over a fixed number of tags T , and is identical to the formulation of tag transitions in the Bayesian HMM (Goldwater and Griffiths, 2007).

the tag sequence is generated by a trigram Dirichlet-multinomial distribution, where transition parameters τ are drawn from a symmetric Dirichlet distribution with the hyperparameter α t .


children clearly learn multiple aspects in parallel, rather than sequentially, implying that models of language acquisition should also incorporate joint learning.


When deciding how to interpret [w@nt], if we posit that the intended vowel is /2/, the word is likely to be /w2n/ "one" and the next word begins with /t/; if instead we posit that the vowel is /O/, the word is probably /wOnt/ "want".

is no cost for the individual characters) 2 .


Although the models with phonetic variability show similar overall segmentation accuracy on noisy data to the original GGJ model, the pattern of errors changes, with less oversegmentation and more un-dersegmentation.

they have difficulty recognizing word tokens that are spoken by a different talker or in a different tone of voice until 11 months (Houston and Jusczyk, 2000;Singh et al., 2004), and the ability to adapt to unfamiliar dialects appears to develop even later, between 15 and 19 months (Best et al., 2009;Heugten and Johnson, in press;White and Aslin, 2011).


Hassan and Menezes (2013) use a random walk framework to capture contextual similarity, which they then interpolate with an edit distance metric.

the unlabeled training data has no such information.


Our character-based model is similar to the phrase-based model, except that, rather than learning word-based mappings from the original tweet and the normalization sentences, we learn characterbased mappings from the original phrases to the normalizations of those phrases.

each segment can be aligned to any word or segment in o.


We observe that better normalizations tend to lead to better translations.

our goal in this section is describe a decoder that uses both models to improve the quality of the normalizations.


This approach implicitly assumes that task difficulty is the only factor affecting the outcomes of competitions (i.e.

we observe that the words 'list' and 'array' have the lowest mean of difficulty scores, compared to the words 'virtual' and 'gcc'.


They provided 18, 196 answers in SO/Math among which 10, 993 (60.41%) were selected as the best answers.

after the common users are linked, we have a joint data set of MO and SO/Math.


Error analysis of our initial runs showed a disturbingly high proportion of responses generated by our system that contained disfluencies with successive repetitions of words such as the and I.

the decoder builds an efficient hypergraph structure which is then scored using the induced grammar.


The PBSMT baseline and the TG model were trained on both the filtered and corrected versions of the training corpus and the quality of the responses were compared.

in hip hop lyrics it is hard to make any linguistic or structural assumptions.


The graphical model for TIR is given in Figure 1, and the generative process is detailed in the following pseudocode: • Sample an influence weight, • For each cited document c ∈ C (d) • Draw edge influence weight, • Assign a prior over topics via • Sample a distribution over topics, The TIR model can be viewed as an adaption of the Dirichlet-multinomial regression (DMR) framework of Mimno and McCallum (2008) to model topical influence.

citation counts are not the whole story.


Hence, in previous work, researchers report their method performance on detecting edited phrases (reparandum) (Johnson and Charniak, 2004).

we model the problem using a deterministic transition-based parser (Nivre, 2008).


Among text-first approaches, the work is split between developing systems which focus specifically on disfluency detection and those which couple disfluency detection with parsing.

we would like to thank Chris Brew, Ron Kaplan, Deepak Ramachandran and Adwait Ratnaparkhi.


This word representation with NLM has been used to good effect, for example in (Turian et al., 2010;Collobert et al., 2011;Huang et al., 2012) where induced word representations are used with sophisticated features to improve performance in various NLP tasks.

we implement cocompositionality using prototype projections on predicates/arguments and show that this is effective in adapting their word representations.


Thus, the change is not both social and rapid, rather, the social change is rapid.

it is worth remarking that MULT approximated the patterns observed in the corpus vectors quite well, despite producing order-insensitive representations of recursive structures.


One possible reason is that the FDLDA model is not specialized for supervised word clustering, and it uses a logistic regression model to predict the labels from the word topics, while the final soft word clustering is computed from the learned distribution p(z) and p(w|z).

for each different s value, we repeated the experiments 10 times by randomly selecting s labeled documents from the target domain using the same experimental setting as before.


max-min normalization: where max and min are computed over all association scores for the entire task for a given measure.

pMIs and spMId perform better than not just pMI, but they perform better than all popular cooccurrence measures on most of these tasks.


We consider only those co-occurrences where span is less than a given threshold.

we find that on average, PMI sig and cPMId, the recently introduced measures that incorporate significance in PMI, do not perform better than PMI on the given datasets.


PMIs improves performance over PMI on three out of four datasets, while sPMId improves performance on all four datasets.

span threshold is a parameter for all the co-occurrence measures being considered.


To address the low-recall issue, recurring cue terms occurring within dictionary and encyclopedic resources can be automatically extracted and incorporated into lexical patterns (Saggion, 2004).

then we apply Gibbs sampling (Blei et al., 2003;Phan et al., 2008) to estimate the probability of each pair (t, g) ∈ M of pertaining to a domain d by computing: where the gloss-domain matrix R M D is initially defined by counting random domain assignments for each word w in the bag of words of each (term, gloss) pair ∈ M .


The results show high precision in both cases.

it is thus applicable to virtually any language of interest.


*We follow the procedure reported in (Lee and Glass, 2012) to set up the HMM prior θ 0 .

the performance of the recognizers is evaluated and compared against three baselines: first, a graphemebased speech recognizer; second, a recognizer built by using an expert-crafted lexicon, which is referred to as an expert lexicon in the rest of the paper for simplicity; and third, a recognizer built by discovering the phonetic units and L2S pronunciation rules sequentially without using a lexicon.


7 enumerate through every frame index in a sentence, treating each feature frame as a potential boundary between acoustic units.

this likelihood is simply the marginal probability of p(x t+1:t+d , n i , c i,p | l i,κ ) with n i and c i,p integrated out, which can be expanded and computed as shown in the last three rows of Eq.


We formulate our approach as a hierarchical Bayesian model, which jointly discovers the acoustic inventory and the latent encoding scheme between the letters and the sounds of a language.

such language-specific knowledge is not available for our training framework; therefore, our strategy is to compile a question list that treats each phonetic unit as a unique linguistic class.


mCTest requires machines to answer multiple-choice reading comprehension questions about fictional stories, directly tackling the high-level goal of open-domain machine comprehension.


The proposed method eliminated only two transliteration candidates in sample-wise but also eliminated 5,714 (0.64%) katakana and 55,737 (4.1%) English characters 3 .

their transliteration mining approach is samplewise; that is, it makes a decision whether a bilingual phrase pair is transliteration or not.


For instance for phrase-based translation, it specifies a hard-limit on the number of hypotheses to retain.

apart from translation decoding, this paper is closely related to work on column generation for NLP.


In prior work, the endmarker symbol has been used to indicate whether an ngram is in the trie.

first, we propose a method for representing backwards suffix trees using double-array structures and demonstrate its efficiency.


As this is the most basic structure based on double-array structures, we refer to it as the simple structure and improve its performance as described in the following sections.

trie-based language models have to trace higher-order ngrams for every query, which requires more time.


Backwards suffix trees (Bell et al., 1990;Stolcke, 2002;Germann et al., 2009) are among the most efficient representations of tries for language models.

hash-based LM such as KenLM probing would repeat queries until finding truncated ngram histories in the trie.


Given an hypergraph, certain hypernodes define a possible solution to the maximization defined in the two IMT formulations.

the translation of a source sentence s is obtained through a generative process composed of three steps: first, the s is divided into K segments (phrases), next, each source phrase,s, is translated into a target phraset, and finally the target phrases are reordered to compose the final translation.


Table 3: IMT results (KSMR [%]) for the EU and TED tasks using the independent suffix formalization (ISF) and the conditioned suffix formalization (CSF).

we also want to compare the proposed IMT approach against a conventional CAT approach without interactivity, such as a decoupled post-edition system.


To control the size of rule  Table 3: Comparison of Max-margin and Max-likelihood estimation on a smaller corpus.

as the difference between the baseline and our max-margin synchronous grammar induction model only lies in the grammar, this result clearly denotes that our learnt grammar does outperform the grammar extracted by the traditional two-step pipeline.


Because rule coverage is important in synchronous grammar induction, we would like to explore the entire derivation space using the average operator.

a target span boundary feature in Figure 2(c), which assesses the target parse structure, is a non-local feature.


Using this tool, we first compare the error distributions across a large set of systems, then analyze common errors across the top ten systems, empirically characterizing the major unsolved challenges of the coreference resolution task.

our analysis tool is available at code.google.com/p/berkeley-coreference-analyser/.


As described in Subsection 5.2, during training and testing, all links associated with zero pronouns will be considered in our coreference resolution system.

in Chinese, a zero pronoun always occurs just before a predicate phrase node (e.g., VP).


zero pronouns are very important in bridging the information gap in a Chinese text.


One limitation of our approach is that it only learns the properties that are present in CSN antecedents.

furthermore, we applied our models to resolve this issue instances from Kolhatkar and Hirst's (2012) work.


Each instance contains three paragraphs from the corresponding NYT article: the paragraph containing the ASN and two preceding paragraphs as context.

aSN antecedents have additional properties which are not always captured by CSN antecedents.


In the experiment we used about 5 million characters from each corpus for efficiency.

besides, our approach uses two unlabeled corpora which can mutually enhancing to get better result.


We use POS tagging and combined POS and morphological (POS+MORPH) tagging to demonstrate the properties and benefits of our approach.

the higher-order models can put less weight on 0-order features as they have access to more context to disambiguate the sequence.


A parser trained on this data will then use the improved morphology as features.

table 6: Syntactic features for featurama (Czech).


Unsupervised learning of concatenative morphology has received extensive attention, partly driven by the MorphoChallenge (Kurimo et al., 2010) in recent years, but that is not the case for root-templatic morphology (Hammarström and Borin, 2011).

the actual rules include certain permutations of these, e.g.


For each adapted non-terminal A ∈ M , a cache C A is maintained for storing the terminating tree fragments expanded from A earlier in the process, and we denote the fragment corresponding to the i-th expansion of A as z i .

as proposal grammar, we use the analogous approximation of our G as Johnson et al.


For example, person UNLOCK door, as illustrated in Table 1.

we report our results in terms of overall accuracy.


mitkov and Barbu (2003) exploit parallel data in English and French to improve pronominal anaphora resolution by combining anaphora resolvers for the individual languages with handwritten rules to resolve conflicts between the output of the language-specific resolvers.


It becomes a regular graph if the cardinalities of both the tail and the head are restricted to one for all hyperarcs.


The perceived visual scene can be represented as a complete hypergraph, in which any pair of two subsets of nodes are connected by a hyperarc.

the importance of group descriptions has been shown not only here, but also in previous works on REG (Funakoshi et al., 2004;Funakoshi et al., 2006;Weijers, 2011).


Given imperfect knowledge of the environment, hypergraphs significantly outperforms the regular graphs by taking advantage of spatial grouping information (p = 0.002).

while our approache performs effectively when the agent has perfect knowledge or perception of the environment (e.g., 84%), it performs poorly under the mismatched perceptual basis (e.g., 45%).


Their representations of the shared world are misaligned.

this assumption no longer holds in situated dialogue with robots.


The class labels have precision comparable to that of class labels derived from human-created knowledge repositories.

instances are deemed questionable, if they would be correct for a rather obscure interpretation of the class label.


As an alternative to extracting class labels from text, some methods simply import them from human-curated resources, for example from the set of categories encoded in Wikipedia (Remy, 2002).

each query is accompanied by its frequency of occurrence.


Class labels derived from Web search queries may be semantically overlapping.

there are around 1.6 million unique "list of .." queries, from which class labels are collected in run R ql .


In the contextual vector of a phrase, the weight of a feature is the pointwise-mutual information (Lin and Wu, 2009) between the phrase P and the feature F .

class labels potentially exhibit higher syntactic diversity.


(2011) in relaxing (5) into a continuous optimization problem and refer the reader to their paper for a more in depth treatment.

relation tuples are generated from a multinomial distribution  each tuple in a document is assigned a hidden relation (z = z 1 ...z N ); each relation is represented by a multinomial distribution over features φ r (Dirichlet prior β).


Wikipedians annotate each entity with categories which provide another source of valuable semantic information.

this model is expressive enough to include any form of features describing the similarity and dissimilarity of d and e. We only include some typical features seen in literature.


Let D be the entire training set: In the next subsection, we will describe how to construct global features from the predictions of g 0 on neighbors N (x) with G. If one wants to identify the correct entity for an ambiguous name, he would possibly look for related entities in its surrounding context.

location name generally is not part of the main topic of one document.


"Barack Obama" with category "Democratic Party presidential nominees" shares the category "United States presidential candidates by party" with "Mitt Romney" when travelling two levels up the network.

we use context as query q and the categories of this candidate entity as d. We also treat the definition page of an entity as its context, and first train the model with definition pages, because definition pages exhibit more focused topic.


Unlike in §5.1, where the true and proposed types could be compared via M&W (Milne and Witten, 2008), they could not be asked to objectively estimate relatedness between types.

two further refinements are needed to make this work.


(Ritter et al., 2011) and (Zheng et al., 2012) are notable exceptions.

the main disambiguation signal is from the immediate entity neighborhood N (e) of the candidate entity e in the Freebase graph.


(Although the pool is small, A0 has very high recall.)


Different from previous approaches that use treeedit information derived from syntactic trees, our kernel-based learning approach also use tree structures but with rather different learning methods, i.e., SVMs and structural kernels, to automatically extract salient syntactic patterns relating questions and answers.

secondly, we establish semantic links between NEs extracted from the answer sentence and the question focus word, which encodes the expected lexical answer type (LAT).


Clearly, having a high recall system, allows for correctly answering more questions.


(Heilman and Smith, 2010) develop an improved Tree Edit Distance (TED) model for learning tree transformations in a q/a pair.

we rely on the shallow tree representation, where text spans identified by a shallow syntactic parser serve as a source of candidate answers.


Also using fewer question classes results in a more accurate multi-class classifier.

their extraction modules basically assume the shape of high-level rules, which are, in any case, essential to achieve state-of-the-art accuracy.


To build a translation graph, the two sets of phrase translation probabilities are represented in the phrase tables.

experimental results show that our method achieves significant improvements over the baseline on europarl corpus, spoken language data and the web data.


For example, Brin and Page (1998) used random walk to discover potential relations between queries and documents for link analysis in information retrieval.

some useful translation relations will be lost.


In this area, most of techniques usually utilize confusion networks (CN) as their central data structure to compact an exponential number of an potential hypotheses, and because better hypothesis alignment may benefit constructing better quality confusion networks, it is natural to add more useful information to improve alignment results.

evolutionary algorithm (Deb et al., 2000;Deb et al., 2002), Tabu search (Hansen, 1997), ants colony (Engelbrecht, 2005), and simulated annealing (Serafini, 1994).


The redefined transitional probability The redefined emission probability We need to note that there is no guarantee on the closed property of probabilities, though these approximations prove to be effective in a practical sense.

this framework is also totally unsupervised.


In this case, the phrase is composed of a sequence of sibling constituents whose common parent is outside the phrase.

to traditional approaches that directly introduce syntactic constraints to translation rules by explicitly decorating them with syntactic annotations, which often exacerbate the data sparsity problem and cause other problems, our approach keeps translation rules intact and factorizes the use of syntactic constraints through two separate models: 1) a syntax mismatch model that associates each nonterminal of a translation rule with a distribution of tags that is used to measure the degree of syntactic compatibility of the translation rule on source spans; 2) a syntax-based reordering model that predicts whether a pair of sibling constituents in the constituent parse tree of the source sentence should be reordered or not when translated to the target language.


We thank the anonymous reviewers for their valuable feedback.

it does not consider possible relationships or dependencies between categories.


Then for each phrase pair, if each token in the source phrase is aligned to a token in the target phrase in a monotonic way, and vice versa, we merge these alignments to form one single phrasal alignment.

showing the superiority of token alignment scores is sufficient.


We compare our system with the top systems reported in the CoNLL shared task 2012 as well as with the Stanford's publicly released rule-based system (Lee et al., 2013;Lee et al., 2011), which won the CoNLL 2011 Shared Task (Pradhan et al., 2011).

when processing mention i, we find whereĈ is the current clustering obtained from the previous inference steps.


While the "must-link" constraints described in the paper can be treated as features, due to their high precision, treating them as hard constraints (set ρ to a high value) is a safe and direct way to inject human knowledge into the learning model.

in the following, we briefly discuss how to solve the inference problem with these two types of constraints.


Performance on named entities: The coreference annotation in Ontonotes 5.0 includes various types of mentions.

cL 3 M scores a mention's contribution to its cluster by combining the corresponding score of the underlying L 3 M model with that from a set of constraints.


This approach is very related to the probabilistic extension of our method as both models attempt to leverage entity-level information from mention-pair features.

we present two models -the Latent Left-Linking Model (L 3 M), and a version of that is augmented with domain knowledge-based constraints, the Constrained Latent Left-Linking Model (CL 3 M).


This is in contrast with the predicted mentions where γ = 0 performed best.

the constraints improve only marginally on the ACE 2004 data because ACE uses shorter phrases as mentions.


, n. object x is mapped to a new feature vector Notice that the original centering formula (2) is recovered by letting w i = 1/n for all i = 1, .


we use a modified version of AdaBoost (Freund and Schapire, 1995) that optimizes for recall.


Table 3 compares our approach to three baselines using the Recall-at-1220 metric evaluated on both the development and test sets.

there are verbs followed by particles that are not phrasal verbs, because their meaning is compositional, such as walk towards, sit behind, or paint on.


For example, PDD gives diverse tags "n" and "vn" for the noun, while CTB just gives "NN".

and if the right tag is one of "n,nt,nz/PDD" but not "n/PDD" (for example, "nt/PDD"), which means it is a "NN/CTB", the weight of "NN/CTB" will remain unchanged according to the algorithm (updating "n/PDD" changes the "NN/CTB", but updating "nt/PDD" changes it back).


nese/posguide.3rd.ch.pdf In this paper, we employ the commonly used label set {B, I, E, S} for the segmentation part of cross-labels and the label of character can be in the form of {B-T}(T represents POS tag).

following (Collins, 2002), the average strategy is used to avoid the overfitting problem.


More interestingly, they clearly define well-delimited lexical fields.

their work focuses on undirected graphs of corpus co-occurrences whereas our work builds on directed associations.


It is interesting to notice at this stage that the EAT seed contains 74% of the words belonging to the USF seed.

for this reason, additional words have been regularly added to the pool of normed words, unfortunately without well established rules being followed.


A colink is a directed cycle of length 2 and a triangle a directed cycle of length 3.

we find that the main strongly connected component of the FA graph (the so-called core) is very cyclic in nature and contains a large predominance of short cycles (i.e., co-links and triangles).


Our system clusters relations with similar named-entity arguments, but this means it does not cluster relations whose arguments are rarely named entities.

rapp (1999) learns to translate words based on small seed bilingual dictionary.


The system only attempts to rerank a very small proportion of sentences, but we believe the coverage could be greatly improved by including relations between common nouns (rather than just namedentities)-future work should explore this.

as we use Wikipedia itself for our training corpora, we can bootstrap entity information directly from its markup.


In our formulation of CDP , correctness of an expanded pair is judged according to the pair's SVM score using BASE.

n , the number of newly added positive training samples during the training data expansion process, was set to 6,000 according to preliminary experiments using the development set.


We collected 35,000 tweets that contain the hashtag #sarcasm or #sarcastic to use as positive instances of sarcasm.

we sort the candidates by their probability and conservatively add only the top 5 positive verb phrases and top 5 positive predicative expressions in each bootstrapping iteration.


Table 2 shows the average length of summary and experience fields we used for evaluating our summarization approach.

we also have five pairwise relationships (e.g., 24 ( , ) yy , ( , ) yy) based on the structure of the input personal profile social network.


Generally, each event contains multiple sub-events and the storyline should be composed by the component summaries of all the sub-events.

we assign each window v with an distribution over local aspects and a distribution ρ defining preference for local aspects versus global aspects.


Here we take the event "Connecticut school shooting" as an example.

different sub-events have different part-whole relationship with the major event, which is important to correspond to users' interests but seldom considered in previous work.


The beta process or the IBP is often used in a feature analysis model to generate infinite vectors of binary indicator variables (Paisley and Carin, 2009), which indicates whether a feature is used to represent a sample.

from fig.1, fig.2 and fig.3, we can see that the result of BNP summarization (the dashed line) gets the second best value among all systems.


Our document content model is primarily based on the global discourse of the article as provided by the type and number of CoreSC categories.

as the emphasis of this approach was the identification of the argumentative zones, less attention was given to the sentence selection criteria for the extractive summaries.


Non-matching answers can be of two types.

the difference here is that we define a-priori certain categories to be independent (have the property of playing the role of nucleus in the discourse) and specify their relation with particular types of dependent categories.


is the set of states that can be reduced with p so that p.s 0 will be the right child of the top tree of the result state.

more formally we define the space of all states S as: Since only the top d+1 trees on the stack are used in atomic features, we only need to remember the necessary information and write the state as: We denote a derivation x's state as [x] ∼ .


Without loss of generality, we assume Algorithm 1 fills C with derivations in the following order: where x 0 is the initial derivation, x m is the first goal derivation in the sequence, and Denote the status of chart right after x k being filled as C k .

to find a better model for best-first search would be an interesting topic for future work.


Due to space constraints we will assume some basic familiarity with shift-reduce parsing; see Nivre (2008) for details.


we require w F ∈ {0.0, 0.1, 0.2, ..., 0.9} and set the constraint F ∈F w F = 1.


The main problem with most available annotated human action datasets is that they are very restricted and domain-specific.

little is known about the performance of these language models when applied to the computer vision domain.


As already mentioned, this result can probably be improved by using a subset of readers or by weighting training examples, e.g., by importance weighting (Shimodaira, 2000).

they use use word lengths and frequencies, like us, as well as distances between tokens (important in a transitionbased model), and, finally, the history of previous decisions.


We can therefore apply standard parsing algorithms to this task.

sE-QUENCE → DURATION before sEQUENCE would be impossible), and some of their primitive elements could naturally be expressed using other primitives (e.g.


An earlier attempt in late 1980's to formalize a rule language resulted in the Common Pattern Specification Language (CPSL) (Appelt and Onyshkevych, 1998).

the community would have shown -objectively -that the problem is not learnable with the available set of constructs, thus motivating follow-on research on extending the standard with new primitives, if possible, or developing novel hybrid IE solutions by leveraging the standard IE rule language together with ML technology.


There is a long history of methods using suface-level lexical patterns for extracting relational facts from text corpora (Hearst, 1992;Brin, 1999;Agichtein and Gravano, 2000;Ravichandran and Hovy, 2002;Etzioni et al., 2004).

we obtained such latent edge labels by mining a large dependency parsed corpus of 500 million web documents and performing PCA on the result.


A method of latent embedding of relation instances for sentence-level relation extraction was shown in (Wang et al., 2011).

it finds features that, e.g., are analogous to the statement "cities in the same state probably lie on the same river".


PRA is then applied over this augmented graph G , over the same set of prediction types R as before.

to such approaches, we focus on how latent embedding of verbs in such triples can be combined with explicit background knowledge to improve coverage of existing KBs.


We first generate the number of source tokens of a translation rule with a uniform distribution for up to, for example, 7 tokens.

instead, we view rules, which are observed from training data, as random variables generated by a hidden model.


• Unsupervised and semi-supervised learning of hidden models.

for each translation r, there exists a hypergraph H r that represents all possible derivations D r = {d} that can generate rule r. Here, each derivation d is a hyperpath using meta- we can use hypergraph H r to characterize r. Translation rules in G can share nodes and meta-rules in their hypergraphs, so that M is more compact model than G. In the rest of this section, we will introduce three methods to quantify H r as features of rule r. It should be noted that there are more ways to exploit the compact model of M than these three.


because the computational complexity of calculating the probabilities of all words is quite high, the CSLM is only used to calculate the probabilities of a subset of the whole vocabulary.


We follow the notations in (Och and Ney, 2002).

we propose a remarkably simple but efficient batch MIRA approach which exploits the exact corpus-level BLEU to compute model losses.


These models were developed to identify the languages of the documents instead of words and we did not retrain them.

the results reported in table 2 were obtained by sampling 5M tokens of each language.


Our approach works as follows: in training, we first use an empirical domain difference testing method to detect whether two groups of data should be considered as different domains; after that we apply supervised clustering to learn the distance metric between two data points, i.e.

our FEDA methods use L 2 -regularized SVM with linear kernel by LIBLINEAR package 1 .


In a final experiment, we look at the Zodiac 340 cipher.

we predict the deciphered text using posterior decoding in the learned HMM.


It could also be the case that a large number of salt tokens were inserted, or that some other assumption is incorrect.

we found that the initializations that lead to the local optima with highest likelihoods are sometimes very rare, but finding them can be worthwhile; for the problems we looked at, local optima with high likelihoods also achieved high accuracies.


The best result is from TF-KLD, with distributional features FEAT 2 , achieving 79.76% accuracy and 85.87% F1.

the feature then has p k = [0.33, 0.67] and q k = [0.32, 0.68], for a KL-divergence of 3.9 × 10 −4 .


A lot of papers have been published for designing appropriate similarity measurements.

given the word w i , w j , we use the notation P (w i ) to denote the occurrence counts of word w i in the corpus C. P (w i , w j ) denotes the co-occurrence counts of word w i and w j in C. In this paper we employ the corpus-based version of the three well-known similarity measurements: Jaccard, Dice and PMI.


As illustrated by Equations 1 and 4, δ(w i , z j ), which represents intervention or help from preexisting knowledge of must-links and cannot-links, plays a key role in this study.

nevertheless, we believe it is possible to do better by using a constrained topic model instead of traditional attribute selection methods.


These mismatches occurred across the range of relation types.

this supplementary material can include unrestricted relative clauses, appositives, or other parenthetical information.


(2008) in that certain bigrams of coherence relation types are significantly more prevalent than others.

obviously, the PDTB does not avail us of the opportunity to evaluate this gradience directly (or even use a 3way required/optional/redundant distinction), since the producer of the actual text samples in the corpus had to ultimately decide whether or not to use a connective.


3 Results and Evaluation The model was evaluated by assessing the accuracy of its predictions against the unseen test set.

our model includes a binary feature (FirstA2Pron) indicating whether the first word in the second argument is a pronoun.


The author and reader (A/R) of a document have not been used for contextual clues because the A/R rarely appear in the discourse in corpora based on newspaper articles, which are main targets of the previous studies.

a zero reference is the zero exophora when a case slot is assigned to pseudo entities.


In English and other languages, the A/R mentions can be detected from coreference information because it can be assumed that the expression that has a coreference relation with first or second personal pronoun is the A/R mention.

we judge that the A/R appear in the discourse if the A/R appear as a referent of zero reference in gold-standard PASs and this judgment is used only in the training phase.


In such a case, the organization is annotated as the author.

such a rough approximation has impeded the zero reference resolution research.


We use a score of the probabilistic PAS analysis (Kawahara and Kurohashi, 2006b) to φ overt-P AS (cf, a, p, t).

in other domain documents such as blog articles and shopping sites, the A/R often appear in the discourse.


Since the dataset is newly created for this task, to compare the performance of the proposed method to other models, we also reimplemented several state-7 http://code.google.com/p/thebeast of-the-art methods for comparison.

both subjective and objective sentences can be used as explanations.


When a user reads an article, the system will then be able to provide her with a thread of events having occurred before or after, helping her to contextualize the information she is reading.

reading more than the first paragraph is sometimes necessary to determine whether a relation exists between two events.


For the competitive task in CoNLL'2010 (Farkas et al, 2010), Morante et al (2010) used a memory-based classifier based on the k-nearest neighbor rule to determine if a token is the first token in a scope sequence, the last, or neither.

most of the previous studies on negation and speculation scope detection task can be divided into two main aspects: the heuristic rule based methods and the machine learning based methods.


Griffiths and Steyvers (2004) analyse evolution of topics over time, but without modelling time explicitly.

this approximation is also known as type II maximum likelihood (ML-II).


• Semantic Match: We compute the concept similarity by measuring the semantic similarity between the two phrases from which the concepts where extracted.

a query reformulation is intended to modify a previous query in hope of getting better results to satisfy the same information need.


Specifically, given a question, a set of queries generated by searchers attempting to answer this question, and a set of documents retrieved by a search engine for each of the queries, our goal to retrieve a set of passages that contain correct answers for the question.

by sharing our code and data, we hope to encourage further research in this area.


This gives us per ranking as many sets as there are letters that change.

note that in EGY, every verb manifests a stem change between the perfective and imperfective forms, while in GER many verbs in fact do have the same stem for all inflected forms.


Throughout, we use a hidden layer size of 100 which provided a good trade-off between time and accuracy in initial experiments.

we also plan to change the cross entropy objective to a BLEU-inspired objective in a discriminative training regime, which we hope to be more effective.


The joint model presented in this paper differs in a number of key aspects: we use a recurrent architecture representing an unbounded history of both source and target words, rather than a feedforward style network.

3 if the context is large, then the amount of recombination will decrease significantly, leading to less variety in the decoder beam.


For each domain, the domain-specific decoder translates each in-domain development sentence and determines the N-best translations (line 4-8).

the setting "[A] G-tM + G-LM" is better than the setting "[A] I-tM + I-LM" across different domains.


, w N that are used for testing (line 27).

for each system, the best translation candidatef is given by: where the translation probability P (f |e) is given by: In-domain where φ j (f, e) is the in-domain feature function and w j is the corresponding feature weight.


(2009), with a post-processing step using our bilingual data cleaning method (Cui et al., 2013).

(2007) tried to weight the training data according to the similarity with test data using information retrieval models, while Foster et al.


Finally, MTL is leveraged to tune multiple systems jointly.

it often lacks some correlation among different domains where common knowledge could be shared to improve the overall translation quality.


With the perceptron algorithm (Shen and Joshi, 2005), the gradient of the hinge loss is: The training instances for the discriminative learning in pairwise ranking are made by comparing the N-best list of the translation candidates scored by the smoothed sentence-level BLEU (Liang et al., 2006).

we use the mixture model based approach proposed by Koehn and Schroeder 1 Many web portal sites contain domain information for webpages, such as "www.yahoo.com" in English and "www.sina.com.cn" in Chinese and etc.


For example, nsp( /NR), tsp(µÈ /NN) and psp(NP 1 ) are consistent while nsp( /JJ) and nsp(µÈ /NN) are not consistent.

as shown in Figure 4, we annotate each node n of phrasal nodes labeled T with two attributes: node span and subtree span; besides, we annotate phrasal span to the parts covered by phrasal nodes in each subtree rooted at n. The three types of spans are defined as follows: Definition 1 Given a node n, its node span nsp(n) is the consecutive target word sequence aligned with the node n. Take the node " /NR" in Figure 4 for example, nsp( /NR)={7-8}, which corresponds to the target words "in" and "asia".


Third, we rank English words by their document-pair co-occurrence score with each French OOV word.

we supplement phrase tables with translations for OOV and low frequency words (we experiment with training data frequencies less than 101, 11, and 1) and include p new k (t|s) and p new k (s|t) as new translation features for those supplemental translations.


For Science, learning is gradual and it appears that additional gains could be made by iterating over even more document pairs.

because we are primarily concerned with learning translations for previously unseen words, we evaluate over OOV French word types.


In Section 5.2 we describe several other baseline approaches.

this prior work stops short of using these lexicons in translation.


In comparison of parsing algorithms, the common practice is to measure the number of edges processed by different algorithms for the same reason (Moore and Dowding, 1991).

the last part of table 4 shows the performance CKY decoder 7 Feature r is defined for SCFG rules and cannot be adopted to phrase-based translation systems; and Moses uses distortion feature therefore we omit Moses from this experiment.


It might appear that the restriction that target-side rules be GNF is a severe restriction on the coverage of possible hypotheses compared to the full set of rules permitted by the Hiero extraction heuristic.

since the glue rules are likely to be used in the top levels (possibly with large distortion) of the derivation, we would want the decoder to learn the distortion for regular and glue rules separately.


Given a query q from author aq and a set of test documents DT, each test document dt i is converted to a s-vector sv i = Sim(dt i , q).

 For Type I, F 1 scores of LSS are markedly better than those of the three baselines.


This indicates that more sample documents give better results in general.

the algorithm for producing the training set, called s-training set, is given in Figure 1.


Note that the authors here are in fact userids.

to reflect sv i is computed based on query q from author aq, a s-test case is thus represented as <(aq, q), (sv i , ?)>.


 Type II: Type II experiments test the cases when no pair exists.

for example, we have the following d-vector for query q: q: 1:1 2:1 6:2 where x:z represents a d-feature x (a word) and its frequency z in q.


Turkish achieved notably high accuracy: the highest of all four languages considered.

9/25 of the k-top female words are pronouns referring to the speaker, a familiar addressee, or a third party (he/she/it), while none of the k-top male words are, suggesting female users are more often talking directly about themselves or to others.


The hybrid models show massive performance in-  creases across the board.

(3) We provide two novel ways to extend the bimodal models to support three or more modalities.


We consider a p value < 0.05 to indicate a statistically significant difference.

scoring with n such grammars now becomes: The distributions q G i still have to be computed independently -and possibly in parallel -but the final decoding can be performed jointly.


(2006), function labels are detrimental for parsing performance for one grammar only.

we present parser accuracy results, measured using Parseval F-score and penn2malt UAS, and, for our systems, function label accuracy for labels produced during parsing and after parsing using Funtag.


The basic settings are a combination of the two following parameters: left or right binarization: we conjecture that this affects the quality of the parsers by impacting the recognition of left and right constituent frontiers.

(RP ) can not reach its maximum at a point where constraints (10) are not satisfied.


Further, assume that the components of the noised feature vectorg(x) are independent.

the key idea behind feature noising is to artificially corrupt the feature vector f (y, x) randomly into somef (y, x) and then maximize the average log-likelihood of y given these corrupted featuresthe motivation is to choose predictors θ that are robust to noise (missing words for example).


As in Teh (2006a), we set different values of a and b for each depth of PYP, but share across all topics and sample values with an auxiliary variable method.

the dependencies among G 0 h and {G k h } are shown in Figure 1.


In (10) p(w|k , h) is the parent distribution of the first selected table, and the other p(s|k ) is the seating arrangement of customers.

wallach's experiments were limited to bigrams, a toy setting for language models, and experiments with higher-order n-grams have not yet been sufficiently studied, which we investigate in this paper.


In each run one set is used as the labelled data S, and the other nine sets are combined and used as the unlabelled data U with segmentations removed.

the features that we use are character n-grams within the neighboring 5-character window and tag bigrams.


We use this segmenter because it is publicly available and it performs well on both the PKU corpus and CU corpus.

we should strive to have the two models less correlated.


In step 5 and 6 during each iteration, some data labelled by M1 are selected and added to the training set L2, and vice versa.

in the CU corpus, where under the CEILING condition the Sun-Segmenter has a much bigger gain over the Char-Segmenter, there is 0.7% improvement by using the data combination approach and 0.8% by using the relabelling approach, and the improvement is statistically significant.


Examples of each tag type are shown in Figure 4, along with the number of tags of each type seen in the training data.

2 while this is a significant percentage of the lexicon, they do not account for the same percentage of tokens during parsing.


After checking all combinations, the combination with the highest score is output.

table 7 shows an example of case alternation between the passive and active voices.


Both representations have their own advantages.

japanese passive sentences can be classified into three types in accordance with what is represented as ga-case in the passive voice: direct passive, indirect passive, and possessor passive.


Passive-to-active voice transformation in English can be performed systematically, which does not depend on lexical information in most cases.

we basically used the same data as Murata et al.


2) In some passive sentences, there are two surface ni-cases as in Example (11).

since the annotation quality was not very high, we manually checked all tags and modified inappropriate ones.


The remainder of the paper is organized as follows: Section 2 discusses the related work.

then, in the retrieved documents, the nouns that immediately precede the pattern are recognized as the hypernyms of X.


Table 5 shows results obtained with the test split not dropping and dropping unbound predicates.

a layered methodology to transform text into logic forms using three logic form transformations modes is presented.


In all cases, parameters are tuned from the training portion of the data.

we will explore (1) compositional operations of vector space models, (2) kernels capturing the non-linear composition of different dimensions in the meaning space, (3) the use of neural networks as an alternative to incorporate nonlinearity in vector composition.


These formulae mimic the ones discussed previously, but they are built under a different assumption: that the saliency (Giora, 1997) of a word's prior polarity might be more related to its posterior polarities score, rather than to sense frequencies.

other works such as (Neviarouskaya et al., 2011) use a fine grained classification approach too, but they consider emotion categories (anger, joy, fear, etc.


Barging in with an answer before that point wastes time and disrupts the user; barging in long after that point also wastes time, but also risks user abandonment.

we thank Anna Shtok for her assistance with the understanding and implementation of the various query prediction metrics.


In fact, both of them involve a measurement of similarity between two story nodes, denoted by two word distributions (see Figure 2).

the failure of structureless system proves that our work of storyline reconstruction makes lots of advantages to help reading.


The corpus also provides a verb that relates the regions, which produces descriptions that are more in line with humangenerated text.

we trained two annotators to draw polygons around the outlines of the regions in an image using the La-belMe annotation tool (Russell et al., 2008).


In the remainder of this section, we will use Figure 1 as a running example to demonstrate the type of language each model is capable of generating.

it has difficulties guessing the correct verb for a description, as it relies on corpus co-occurrences for this (see the second example in Table 4).


These evaluation measures were selected because recall and precision are not sensitive to variations of segment length contrary to the Pk measure (Beeferman et al., 1997) and do not favor segmentations with a few number of frontiers as WindowDiff (Pevzner and Hearst, 2002) (see (Niekrasz and Moore, 2010) for a rigorous analytical explanation of the biases of Pk and Win-dowDiff ).

while global methods override these drawbacks, they face the problem of over-segmentation due to the fact that they mainly rely on the sole lexical cohesion.


Local methods (Hearst, 1997;Ferret et al., 1998;Hernandez and Grau, 2002;Claveau and Lefèvre, 2011) locally compare adjacent fixed size regions, claiming a boundary when the similarity between the adjacent regions is small enough, thus identifying points of high lexical disruption.

transcripts significantly differ from written texts: no punctuation signs or capital letters; no sentence structure but rather utterances which are only loosely syntactically motivated; presence of transcription errors which may imply an accentuated lack of word repetitions.


Establishing the brand in the broad social context is just as important as building a good product (Makens, 1965;Lederer and Hill, 2001;Kim et al., 2013).

these studies all suggest that brand and its associations play important roles in the customers' perceptions and decisions.


(2007) are among the first to investigate graph Laplacians as a manifold regularization method for statistical learning.

these features stopped to show up on the list of Dunkin' Donuts.


Similar to Information Retrieval, we also consider using Mean Average Precision (MAP) as metrics to measure the overall quality of retrieved products.

the reason is that pseudo relevance feedback relies highly on top results and a system with better search quality will benefit more from it.


Recall that in Section 2 we use the pattern based method to extract product keywords from tweets related to a trend.

we can compute the similarity between a pair of products.


Axis I in the DSM-IV includes all the major diagnostic categories, excluding mental retardation and personality disorders.

some individuals are motivated to underreport symptoms to avoid negative consequences (e.g.


A narrative event chain is a partially ordered set of events related by a common protagonist (Chambers and Jurafsky, 2008).

according to K&Z, ZPs do not need to be extracted from every VP: if a VP node occurs in a coordinate structure or is modified by an adverbial node, then only its parent VP node needs to be considered.


To confirm our results we also run experiments on the German→English task of the IWSLT 2012 evaluation campaign 2 .

the 4-gram LM is trained on the tED, Europarl and news-commentary corpora.


Data sparsity is one of the major problems for statistical learning methods in natural language processing (NLP) today.

in this work each word is mapped to a single class, which means that when we have selected a translation option for the surface form, the target side on the word class level is predetermined.


NPLMs have previously been applied to MT, most notably feed-forward NPLMs (Schwenk, 2007;Schwenk, 2010) and RNN-LMs (Mikolov, 2012).

in one case (Spanish-English), integrated decoding scored higher than reranking on the development data but lower on the test data -perhaps due to the difference in domain between the two.


Except where noted, we ran MERT three times and report the average score.

we defined a special word <null> whose embedding is the weighted average of the (input) embeddings of all the other words in the vocabulary.


A small validation set of word similarities from (Jin and Wu, 2012) is used to ensure the embeddings have reasonable semantics.

we use the following equation to compute starting word embeddings: In this equation, S is the number of possible target language words that are aligned with the source word.


For example, in Figure 1, G(w 8 ) = (g 1 , g 3 , g 8 ).

we have proposed a pair of language models which are probabilistic models for the lexicalisation of a given dependency tree.


Previous idiom detection systems fall in one of two paradigms: phrase classification, where a phrase p is always idiomatic or literal, e.g.

other points are possible, especially when aiming for high precision to extend the labels in Wiktionary.


Closest to our work are approaches that present token and sentence splitters using conditional random fields (Tomanek et al., 2007;Fares et al., 2013).

we use this feature combination in the experiments that follow.


We introduce an extended naive Bayes model for word sense induction (WSI) and apply it to a WSI task.

v-Measure is between 0 and 1; higher is better.


Thus, these appear to be cases of disagreement, either because the correct intuition requires more work to obtain or because of differences across idiolects (at the moment, there is no obvious pattern as to which sentences caused difficulty, but the sample size is small).

after six weeks, 555 volunteers had provided at least one annotation, for a total of 39,274 annotations, demonstrating the feasibility of collecting large numbers of annotations through this method.


(2006)), thereby motivating restaurants to improve their sanitary practice.

no prior work has examined the utility of review analysis as a predictive tool for accessing hygiene of restaurants, perhaps because the connection is not entirely conspicuous: after all, customers are neither familiar with inspection codes, nor have the full access to the kitchen, nor have been asked to report on the hygiene aspects of their expe-  rience.


Clearly, the mere use of a second-order method improves results, regardless of the base measure.

similar methods have also been used in the field of intrinsic plagiarism detection, which involves segmenting a text and then identifying outlier segments (stamatatos, 2009;.


Following a common approach to structured prediction problems, the score of a tree y is defined as a sum of local scores.

our approach has a similar flavor to cascaded classifiers (Viola and Jones, 2004;Weiss and Taskar, 2010) in that we make decisions for each edge at every stage.


We have 2000 test documents from the target language domain as testing data.

the values of α, β, γ and η are selected using the first cross language classification task GB.


ILP 2-step refers to the 2-step fast decoding strategy proposed by (Berg-Kirkpatrick et al., 2011).

the actual scale of ILP is rather small after pruning (e.g., 176 variables and 372 constraints per topic).


Figure 4 shows the example summaries generated by TKP(G), Marcu(G), MCP and LEAD, respectively for an article, wsj 1128.

a nucleus is more important than a satellite in terms of the writer's purpose.


The Delicious hegemony won't end anytime soon.

we first propose rules that transform an RST-DT into a dependency-based discourse tree (DEP-DT) that explicitly defines the parent-child relationships.


More than twice as many Red Delicious apples are grown as the Golden variety, America's No.

we cannot judge whether we have to drop e 9 or e 10 because the parent-child relationships are not explicitly defined between e 8 and e 9 , e 8 and e 10 .


line 8-16 iteratively put questions in corresponding clusters of cet e .


For example, "such as NP, NP" is a good pattern for detecting similar entities using first-order co-occurrences.

we further evaluate the effectiveness of CET in structuralizing UGC from both user and system aspects.


With the development of Web 2.0, social media websites-such as online forums, blogs, microblogs, social networks, and community question answering (CQA) portals-have become the mainstream of web, where users create, share, and exchange information with each other.

if the candidate list is empty, a new cluster with e i as the element will be created.


Otherwise, we identify the key entity in q (Line 6) and construct the CET cet e whose root entity is e (Line 7).

to the best of our knowledge, no previous study leverages entities to organize UGC in social media.


Because the CET-based program provides a series of clustered entities, it helpes users further refine queries through clicking on entities rather than reconstructing a new query.

more and more UGC is accumulated, with social media websites retaining a huge amount of human knowledge and user experience.


Select one entity and create a new cluster which contains the entity; 2.

both the browsing and the searching give the user a list of relevant contents (e.g., questions shown in Table 1), not the direct knowledge.


Accuracy drops when POS tag features are omitted, e.g., in the question "What number is Kevin Youkilis on the Boston Red Sox" the parser happily skips the NNPs "Kevin Youkilis" and returns the numbers of all players on the Boston Red Sox.

we now evaluate our semantic parser empirically.


Denotation features While it is clear that learning from denotations rather than logical forms is a drawback since it provides less information, it is less obvious that working with denotations actually gives us additional information.

we started with the question "where was Barack Obama born?"


Two nearly identical posts belong in different categories if one mentions a patient and the other does not.

we then conducted a series of experiments for the document classification task: distinguishing patientspecific message board posts from general posts.


From the source documents of the selected corpora, we extract 3.52M lexical chains.

we used a hierarchical phrased-based SMT system (Chiang, 2007) trained on large-scale data.


Given a source lexical chain LC s = {s j i } where the ith chain word s j i is from the jth sentence of the source document D s , we generate a target lexical chain LC t = {t j i } using maximum entropy (Max-Ent) classifiers.

where C represents { LC k t } N k=1 , and the δ function is defined as follows.


First, we modify the objective function to be .

a parameter λ > 0 used in the definition of log .


If we perform a transduction from p toḡ, the weights from the transducer provide the (negative log) joint probability P(p,ḡ).

standard lexicons have been used in recent years with machine translation systems such as Moses (Koehn et al., 2007), to train g2p systems (Laurent et al., 2009;Gerosa and Federico, 2009).


tor via a linear combination of all the elements of the input vector (right part of Figure 1); in other words, possible interdependencies between different features are also taken into account, offering (in principle) more power.

what we really want is a vector where all the dissolving-related components will be eliminated, since they are irrelevant to the way the word 'run' is used in the sentence.


similarity (Deerwester et al., 1990) and relational similarity (Turney, 2006;Zhila et al., 2013;.

figure 2 illustrates this design using a simplified matrix as example.


LSA can be used to compute the similarity between two documents or two words in the latent space.

in principle, the raw information in the input tensor W can be used for computing lexical similarity using the cosine score between the column vectors for two words from the same slice of the tensor.


This is important because Tucker decomposition minimizes the reconstruction error (the Frobenius norm of the residual tensor).

the lists below show the hyponyms and their respective MRLSA scores for each of the question words as determined by MRLSA:4-layers.